RegCM4对中国东部区域气候模拟的辐射收支分析

利用卫星和再分析数据,评估了区域气候模式Reg CM4对中国东部地区辐射收支的基本模拟能力,重点关注地表净短波(SNS)、地表净长波(SNL)、大气顶净短波(TNS)、大气顶净长波(TNL)4个辐射分量。结果表明:1)短波辐射的误差值在夏季较大,而长波辐射的误差值在冬季较大。但各辐射分量模拟误差的空间分布在冬、夏季都有较好的一致性。2)对于地表辐射通量,SNS表现为正偏差(向下净短波偏多),在各分量中误差最大,区域平均误差值近50 W/m2;SNL表现为负偏差(向上净长波偏多);对于大气顶辐射通量,TNS和TNL分别表现为"北负南正"的误差分布和整体正偏差。3)利用空间相关和散点线性回归方法对4个辐射分量的模拟误差进行归因分析,发现在云量、地表反照率、地表温度三个直接影响因子中,云量模拟误差的贡献最大,中国东部地区云量模拟显著偏少。     

The surface radiation budget over South America in a set of regional climate models from the CLARIS-LPB project

The performance of seven regional climate models in simulating the radiation and heat fluxes at the surface over South America (SA) is evaluated. Sources of uncertainty and errors are identified. All simulations have been performed in the context of the CLARIS-LPB Project for the period 1990–2008 and are compared with the GEWEX-SRB, CRU, and GLDAS2 dataset and NCEP-NOAA reanalysis. Results showed that most of the models overestimate the net surface short-wave radiation over tropical SA and La Plata Basin and underestimate it over oceanic regions. Errors in the short-wave radiation are mainly associated with uncertainties in the representation of surface albedo and cloud fraction. For the net surface long-wave radiation, model biases are diverse. However, the ensemble mean showed a good agreement with the GEWEX-SRB dataset due to the compensation of individual model biases. Errors in the net surface long-wave radiation can be explained, in a large proportion, by errors in cloud fraction. For some particular models, errors in temperature also contribute to errors in the net long-wave radiation. Analysis of the annual cycle of each component of the energy budget indicates that the RCMs reproduce generally well the main characteristics of the short- and long-wave radiations in terms of timing and amplitude. However, a large spread among models over tropical SA is apparent. The annual cycle of the sensible heat flux showed a strong overestimation in comparison with the reanalysis and GLDAS2 dataset. For the latent heat flux, strong differences between the reanalysis and GLDAS2 are calculated particularly over tropical SA.     

Representing glaciers in a regional climate model

A glacier parameterization scheme has been developed and implemented into the regional climate model REMO. The new scheme interactively simulates the mass balance as well as changes of the areal extent of glaciers on a subgrid scale. The temporal evolution and the general magnitude of the simulated glacier mass balance in the European Alps are in good accordance with observations for the period 1958–1980, but the strong mass loss towards the end of the twentieth century is systematically underestimated. The simulated decrease of glacier area in the Alps between 1958 and 2003 ranges from −17.1 to −23.6%. The results indicate that observed glacier mass balances can be approximately reproduced within a regional climate model based on simplified concepts of glacier-climate interaction. However, realistic results can only be achieved by explicitly accounting for the subgrid variability of atmospheric parameters within a climate model grid box.     

The simulation of medicanes in a high-resolution regional climate model

Medicanes, strong mesoscale cyclones with tropical-like features, develop occasionally over the Mediterranean Sea. Due to the scarcity of observations over sea and the coarse resolution of the long-term reanalysis datasets, it is difficult to study systematically the multidecadal statistics of sub-synoptic medicanes. Our goal is to assess the long-term variability and trends of medicanes, obtaining a long-term climatology through dynamical downscaling of the NCEP/NCAR reanalysis data. In this paper, we examine the robustness of this method and investigate the value added for the study of medicanes. To do so, we performed several climate mode simulations with a high resolution regional atmospheric model (CCLM) for a number of test cases described in the literature. We find that the medicanes are formed in the simulations, with deeper pressures and stronger winds than in the driving global NCEP reanalysis. The tracks are adequately reproduced. We conclude that our methodology is suitable for constructing multi-decadal statistics and scenarios of current and possible future medicane activities.     

Alpine snow cover in a changing climate: a regional climate model perspective

An analysis is presented of an ensemble of regional climate model (RCM) experiments from the ENSEMBLES project in terms of mean winter snow water equivalent (SWE), the seasonal evolution of snow cover, and the duration of the continuous snow cover season in the European Alps. Two sets of simulations are considered, one driven by GCMs assuming the SRES A1B greenhouse gas scenario for the period 1951–2099, and the other by the ERA-40 reanalysis for the recent past. The simulated SWE for Switzerland for the winters 1971–2000 is validated against an observational data set derived from daily snow depth measurements. Model validation shows that the RCMs are capable of simulating the general spatial and seasonal variability of Alpine snow cover, but generally underestimate snow at elevations below 1,000 m and overestimate snow above 1,500 m. Model biases in snow cover can partly be related to biases in the atmospheric forcing. The analysis of climate projections for the twenty first century reveals high inter-model agreement on the following points: The strongest relative reduction in winter mean SWE is found below 1,500 m, amounting to 40–80 % by mid century relative to 1971–2000 and depending upon the model considered. At these elevations, mean winter temperatures are close to the melting point. At higher elevations the decrease of mean winter SWE is less pronounced but still a robust feature. For instance, at elevations of 2,000–2,500 m, SWE reductions amount to 10–60 % by mid century and to 30–80 % by the end of the century. The duration of the continuous snow cover season shows an asymmetric reduction with strongest shortening in springtime when ablation is the dominant factor for changes in SWE. We also find a substantial ensemble-mean reduction of snow reliability relevant to winter tourism at elevations below about 1,800 m by mid century, and at elevations below about 2,000 m by the end of the century.     

Spectral nudging sensitivity experiments in a regional climate model

In this study, the scale selective bias correction (SSBC) method described by Kanamitsu et al. (2010) is further examined by considering the full wind nudging and the vertically weighted damping coefficient. The 2001 June?CJuly?CAugust RSM simulation over a relatively large domain covering much of the Asian continent, the northern part of Australia, and the Indian and western Pacific oceans was the main focus. The full wind nudging shows wind fields closer to the driving global analysis. However, it leads to significantly distorted fields (e.g., temperature and geopotential height) aloft, accompanying excessive precipitation over the western Pacific. The gradual reduction of vorticity nudging from the model top to the ground surface improves rainfall patterns without a discernible distortion of large-scale fields. Further evaluation of a 10-year-summer simulation over East Asia confirmed that this revised SSBC method improves the monsoonal rainfall against the method of Kanamitsu et al. It is therefore concluded that vorticity nudging alleviates largescale errors by maintaining the near geostrophic balance between mass and winds. The reduction of this nudging factor in the lower troposphere allows the ageostrophic component of wind to develop as in nature, which leads to the improvement of precipitation.     

An evaluation of the surface radiation budget over North America for a suite of regional climate models against surface station observations

Components of the surface radiation budget (SRB) [incoming shortwave radiation (ISR) and downwelling longwave radiation (DLR)] and cloud cover are assessed for three regional climate models (RCM) forced by analysed boundary conditions, over North America. We present a comparison of the mean seasonal and diurnal cycles of surface radiation between the three RCMs, and surface observations. This aids in identifying in what type of sky situation simulated surface radiation budget errors arise. We present results for total-sky conditions as well as overcast and clear-sky conditions separately. Through the analysis of normalised frequency distributions we show the impact of varying cloud cover on the simulated and observed surface radiation budget, from which we derive observed and model estimates of surface cloud radiative forcing. Surface observations are from the NOAA SURFRAD network. For all models DLR all-sky biases are significantly influenced by cloud-free radiation, cloud emissivity and cloud cover errors. Simulated cloud-free DLR exhibits a systematic negative bias during cold, dry conditions, probably due to a combination of omission of trace gas contributions to the DLR and a poor treatment of the water vapor continuum at low water vapor concentrations. Overall, models overestimate ISR all-sky in summer, which is primarily linked to an underestimate of cloud cover. Cloud-free ISR is relatively well simulated by all RCMs. We show that cloud cover and cloud-free ISR biases can often compensate to result in an accurate total-sky ISR, emphasizing the need to evaluate the individual components making up the total simulated SRB.     

Summer dryness in a warmer climate: a process study with a regional climate model

Earlier GCM studies have expressed the concern that an enhancement of greenhouse warming might increase the occurrence of summer droughts in mid-latitudes, especially in southern Europe and central North America. This could represent a severe threat for agriculture in the regions concerned, where summer is the main growing season. These predictions must however be considered as uncertain, since most studies featuring enhanced summer dryness in mid-latitudes use very simple representations of the land-surface processes ("bucket" models), despite their key importance for the issue considered. The current study uses a regional climate model including a land-surface scheme of intermediate complexity to investigate the sensitivity of the summer climate to enhanced greenhouse warming over the American Midwest. A surrogate climate change scenario is used for the simulation of a warmer climate. The control runs are driven at the lateral boundaries and the sea surface by reanalysis data and observations, respectively. The warmer climate experiments are forced by a modified set of initial and lateral boundary conditions. The modifications consist of a uniform 3 K temperature increase and an attendant increase of specific humidity (unchanged relative humidity). This strategy maintains a similar dynamical forcing in the warmer climate experiments, thus allowing to investigate thermodynamical impacts of climate change in comparative isolation. The atmospheric CO ₂ concentration of the sensitivity experiments is set to four times its pre-industrial value. The simulations are conducted from March 15 to October 1st, for 4 years corresponding to drought (1988), normal (1986, 1990) and flood (1993) conditions. The numerical experiments do not present any great enhancement of summer drying under warmer climatic conditions. First, the overall changes in the hydrological cycle (especially evapotranspiration) are of small magnitude despite the strong forcing applied. Second, precipitation increases in spring lead to higher soil water recharge during this season, compensating for the enhanced soil moisture depletion occurring later in the year. Additional simulations replacing the plant control on transpiration with a bucket-type formulation presented increased soil drying in 1988, the drought year. This suggests that vegetation control on transpiration might play an important part in counteracting an enhancement of summer drying when soil water gets limited. Though further aspects of this issue would need investigating, our results underline the importance of land-surface processes in climate integrations and suggest that the risk of enhanced summer dryness in the region studied might be less acute than previously assumed, provided the North American general circulation does not change markedly with global warming.     

Extremes and predictability in the European pre-industrial climate of a regional climate model

A high-resolution pre-industrial control simulation with the regional climate model REMO is analyzed in detail for different European subregions. To our knowledge, this is the first long pre-industrial control simulation by a regional climate model as well as at comparable resolution. We assess the ability of the climate model to reproduce the observed climate variability in various parts of the continent. In order to investigate the representation of extreme events in the model under pre-industrial greenhouse gas concentrations, selected seasons are examined with regard to the atmospheric circulation and other climatic characteristics that have contributed to the occurrences. A special focus is dedicated to land-atmosphere interactions. Extreme seasons are simulated by the model under various circumstances, some of them strongly resemble observed periods of extraordinary conditions like the summer 2003 or autumn 2006 in parts of Europe. The regional perspective turns out to be of importance when analyzing events that are constituted by meso-scale atmospheric dynamics. Moreover, the predictability of the European climate on seasonal to decadal time scales is examined by relating the statistics of surface variables to large-scale modes of variability impacting the North Atlantic sector like the Meridional Overturning Circulation, the El Niño Southern Oscillation, and the North Atlantic Oscillation. For this purpose, we introduce a measure of tail dependence that quantifies the correlation between extreme values in two variables that describe the state of the climate system. Significant dependence of extreme events can be detected in various situations.     

Internal variability in a regional climate model over West Africa

Sensitivity studies with regional climate models are often performed on the basis of a few simulations for which the difference is analysed and the statistical significance is often taken for granted. In this study we present some simple measures of the confidence limits for these types of experiments by analysing the internal variability of a regional climate model run over West Africa. Two 1-year long simulations, differing only in their initial conditions, are compared. The difference between the two runs gives a measure of the internal variability of the model and an indication of which timescales are reliable for analysis. The results are analysed for a range of timescales and spatial scales, and quantitative measures of the confidence limits for regional model simulations are diagnosed for a selection of study areas for rainfall, low level temperature and wind. As the averaging period or spatial scale is increased, the signal due to internal variability gets smaller and confidence in the simulations increases. This occurs more rapidly for variations in precipitation, which appear essentially random, than for dynamical variables, which show some organisation on larger scales.     

The impact of lateral boundary data errors on the simulated climate of a nested regional climate model

In this study, we investigate the response of a Regional Climate Model (RCM) to errors in the atmospheric data used as lateral boundary conditions (LBCs) using a perfect-model framework nick-named the “Big-Brother Experiment” (BBE). The BBE has been designed to evaluate the errors due to the nesting process excluding other model errors. First, a high-resolution (45 km) RCM simulation is made over a large domain. This simulation, called the Perfect Big Brother (PBB), is driven by the National Centres for Environmental Prediction (NCEP) reanalyses; it serves as reference virtual-reality climate to which other RCM runs will be compared. Next, errors of adjustable magnitude are introduced by performing RCM simulations with increasingly larger domains at lower horizontal resolution (90 km mesh). Such simulations with errors typical of today’s Coupled General Circulation Models (CGCM) are called the Imperfect Big-Brother (IBB) simulations. After removing small scales in order to achieve low-resolution typical of today’s CGCMs, they are used as LBCs for driving smaller domain high-resolution RCM runs; these small-domain high-resolution simulations are called Little-Brother (LB) simulations. The difference between the climate statistics of the IBB and those of PBB simulations mimic errors of the driving model. The comparison of climate statistics of the LB to those of the PBB provides an estimate of the errors resulting solely from nesting with imperfect LBCs. The simulations are performed over the East Coast of North America using the Canadian RCM, for five consecutive February months (from 1990 to 1994). It is found that the errors contained in the large scales of the IBB driving data are transmitted to and reproduced with little changes by the LB. In general, the LB restores a great part of the IBB small-scale errors, even if they do not take part in the nesting process. The small scales are seen to improve slightly in regions with important orographic forcing due to the finer resolution of the RCM. However, when the large scales of the driving model have errors, the small scales developed by the LB have errors as well, suggesting that the large scales precondition the small scales. In order to obtain correct small scales, it is necessary to provide the accurate large-scale circulation at the lateral boundary of the RCM.     

Present climate simulation over Korea with a regional climate model using a one-way double-nested system

Summary This study investigates the capability of the regional climate model RegCM3 to simulate surface air temperature and precipitation over the Korean Peninsula. The model is run in one-way double nested mode, with a 60 km grid point spacing “mother” domain encompassing the eastern regions of Asia and a 20 km grid point spacing nested domain covering the Korean Peninsula. The simulation spans the three-year period of 1 October 2000 through 30 September 2003 and the boundary conditions needed to run the mother domain experiment are provided from the NCEP reanalysis of observations. The model results are compared with a high density station observation dataset to examine the fine scale structure of the surface climate signal. The model shows a good performance in capturing both the sign and magnitude of the seasonal and inter-annual variations of the surface variables both over East Asia as a whole and over the Korean Peninsula in the nested system. Some persistent biases are however present. Surface temperature is systematically underestimated, especially over mountainous regions in the warm season. This feature may be due to the relatively coarse representation of the Korean topography. The simulated precipitation over the mother domain successfully reproduces the broad spatial pattern of observed precipitation over East Asia along with its seasonal evolution. On the other hand, fine scale details from the nested results show a varying level of quality for the different individual years. Because of the better resolved topographic forcing, the increased resolution of the nested model improves the spatial agreement with the fine scale observation fields for temperature and cold season precipitation. For summer monsoon precipitation the simulation of individual monsoon convective events and tropical storms is however more important than the topographic forcing, and therefore the performance of the nested system is more case-dependent.     

Multidecadal simulation of coastal fog with a regional climate model

In order to model stratocumulus clouds and coastal fog, we have coupled the University of Washington boundary layer model to the regional climate model, RegCM (RegCM-UW). By comparing fog occurrences observed at various coastal airports in the western United States, we show that RegCM-UW has success at modeling the spatial and temporal (diurnal, seasonal, and interannual) climatology of northern California coastal fog. The quality of the modeled fog estimate depends on whether coast-adjacent ocean or land grid cells are used; for the model runs shown here, the oceanic grid cells seem to be most appropriate. The interannual variability of oceanic northern California summertime fog, from a multi-decadal simulation, has a high and statistically significant correlation with the observed interannual variability (r = 0.72), which indicates that RegCM-UW is capable of investigating the response of fog to long-term climatological forcing. While RegCM-UW has a number of aspects that would benefit from further investigation and development, RegCM-UW is a new tool for investigating the climatology of coastal fog and the physical processes that govern it. We expect that with appropriate physical parameterizations and moderate horizontal resolution, other climate models should be capable of simulating coastal fog. The source code for RegCM-UW is publicly available, under the GNU license, through the International Centre for Theoretical Physics.     

冰晶粒子不同形状假定对辐射收支和气候的影响

将包含多形状冰晶粒子的冰云辐射参数化方案应用于全球气候模式中,详细讨论了冰云粒子从球形假定到多形状假定的变化对辐射场和气候场的影响。结果显示,冰晶粒子形状假定的引入对冰云光学厚度、辐射通量和加热率以及温度场均有明显的影响。采用新的冰云方案使得全球平均云光学厚度值降低0.28（23%）;热带地区降低最为明显,其差异绝对值可达1.02,而在中高纬度陆地地区,两者的冰云光学厚度差别较小。冰晶粒子形状假定改变将导致全球平均的大气顶出射长波辐射通量增加5.52 W/m2（2.3%）。与观测资料的比较表明,多形状冰晶粒子假定明显减小了球形粒子假定对长波出射辐射的低估。对大气加热率廓线的模拟显示,多形状冰晶粒子假定会减弱短波辐射对大气的加热作用,同时增强长波辐射对大气的冷却作用;在热带对流层中高层,这两种影响尤为显著。冰晶粒子形状假定的改变对温度场有明显的影响,热带地区的对流层高层大气温度降低幅度可超过1.5 K。研究表明,冰晶粒子形状假定的改变对模拟的辐射和温度场均有重要的影响。      

The simulation of cutoff lows in a regional climate model: reliability and future trends

Cutoff lows are an important source of rainfall in the mid-latitudes that climate models need to simulate accurately to give confidence in climate projections for rainfall. Coarse-scale general circulation models used for climate studies show some notable biases and deficiencies in the simulation of cutoff lows in the Australian region and important aspects of the broader circulation such as atmospheric blocking and the split jet structure observed over Australia. The regional climate model conformal cubic atmospheric model or CCAM gives an improvement in some aspects of the simulation of cutoffs in the Australian region, including a reduction in the underestimate of the frequency of cutoff days by more than 15 % compared to a typical GCM. This improvement is due at least in part to substantially higher resolution. However, biases in the simulation of the broader circulation, blocking and the split jet structure are still present. In particular, a northward bias in the central latitude of cutoff lows creates a substantial underestimate of the associated rainfall over Tasmania in April to October. Also, the regional climate model produces a significant north–south distortion of the vertical profile of cutoff lows, with the largest distortion occurring in the cooler months that was not apparent in GCM simulations. The remaining biases and presence of new biases demonstrates that increased horizontal resolution is not the only requirement in the reliable simulation of cutoff lows in climate models. Notwithstanding the biases in their simulation, the regional climate model projections show some responses to climate warming that are noteworthy. The projections indicate a marked closing of the split jet in winter. This change is associated with changes to atmospheric blocking in the Tasman Sea, which decreases in June to November (by up to 7.9 m s^?1), and increases in December to May. The projections also show a reduction in the number of annual cutoff days by 67 % over the century, together with an increase in their intensity, and these changes are strongest in spring and summer.     

Elevation gradients of European climate change in the regional climate model COSMO-CLM

A transient climate scenario experiment of the regional climate model COSMO-CLM is analyzed to assess the elevation dependency of 21st century European climate change. A focus is put on near-surface conditions. Model evaluation reveals that COSMO-CLM is able to approximately reproduce the observed altitudinal variation of 2 m temperature and precipitation in most regions and most seasons. The analysis of climate change signals suggests that 21st century climate change might considerably depend on elevation. Over most parts of Europe and in most seasons, near-surface warming significantly increases with elevation. This is consistent with the simulated changes of the free-tropospheric air temperature, but can only be fully explained by taking into account regional-scale processes involving the land surface. In winter and spring, the anomalous high-elevation warming is typically connected to a decrease in the number of snow days and the snow-albedo feedback. Further factors are changes in cloud cover and soil moisture and the proximity of low-elevation regions to the sea. The amplified warming at high elevations becomes apparent during the first half of the 21st century and results in a general decrease of near-surface lapse rates. It does not imply an early detection potential of large-scale temperature changes. For precipitation, only few consistent signals arise. In many regions precipitation changes show a pronounced elevation dependency but the details strongly depend on the season and the region under consideration. There is a tendency towards a larger relative decrease of summer precipitation at low elevations, but there are exceptions to this as well.     

Quantifying Arctic contributions to climate predictability in a regional coupled ocean-ice-atmosphere model

The relative importance of regional processes inside the Arctic climate system and the large scale atmospheric circulation for Arctic interannual climate variability has been estimated with the help of a regional Arctic coupled ocean-ice-atmosphere model. The study focuses on sea ice and surface climate during the 1980s and 1990s. Simulations agree reasonably well with observations. Correlations between the winter North Atlantic Oscillation index and the summer Arctic sea ice thickness and summer sea ice extent are found. Spread of sea ice extent within an ensemble of model runs can be associated with a surface pressure gradient between the Nordic Seas and the Kara Sea. Trends in the sea ice thickness field are widely significant and can formally be attributed to large scale forcing outside the Arctic model domain. Concerning predictability, results indicate that the variability generated by the external forcing is more important in most regions than the internally generated variability. However, both are in the same order of magnitude. Local areas such as the Northern Greenland coast together with Fram Straits and parts of the Greenland Sea show a strong importance of internally generated variability, which is associated with wind direction variability due to interaction with atmospheric dynamics on the Greenland ice sheet. High predictability of sea ice extent is supported by north-easterly winds from the Arctic Ocean to Scandinavia.